Evidence map›Paper›PMID 36672135›Full record

ArticleBrain sciences2023

Screening Children's Intellectual Disabilities with Phonetic Features, Facial Phenotype and Craniofacial Variability Index.

Yuhe Chen, Simeng Ma, Xiaoyu Yang, Dujuan Liu, Jun Yang

Open access · goldAbstract read
In one paragraph

Article in Brain sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.6field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 5 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 1 country.

Yuhe ChenSchool of Foreign Languages, Huazhong University of Science and Technology, Wuhan 430074, China.
Simeng MaDepartment of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China.
Xiaoyu YangDepartment of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Dujuan LiuDepartment of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China.
Jun YangSchool of Computer Science & Technology, Huazhong University of Science and Technology, Wuhan 430074, China.ORCID 0000-0003-1921-7915
Wuhan University · CNHuazhong University of Science and Technology · CNUnion Hospital · CNWuhan University of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIntellectual Disability (ID) is a kind of developmental deficiency syndrome caused by congenital diseases or postnatal events. This syndrome could be intervened as soon as possible if its early screening was efficient, which may improve the condition of patients and enhance their self-care ability. The early screening of ID is always achieved by clinical interview, which needs in-depth participation of medical professionals and related medical resources.

methodsA new method for screening ID has been proposed by analyzing the facial phenotype and phonetic characteristic of young subjects. First, the geometric features of subjects' faces and phonetic features of subjects' voice are extracted from interview videos, then craniofacial variability index (CVI) is calculated with the geometric features and the risk of ID is given with the measure of CVI. Furthermore, machine learning algorithms are utilized to establish a method for further screening ID based on facial features and phonetic features.

resultsThe proposed method using three feature sets, including geometric features, CVI features and phonetic features was evaluated. The best performance of accuracy was closer to 80%.

conclusionsThe results using the three feature sets revealed that the proposed method may be applied in a clinical setting in the future after continuous improvement.

Indexed as

craniofacial variability indexfacial featuresintellectual disabilitymachine learningphonetic features

Identifiers

PMID36672135
PMCPMC9857173
OpenAlexW4316466734

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.